[34] Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, and Jan Peters. An algorithmic perspective on imitation learning. Foundations and Trends in Robotic, 7(1-2):1-179, 2018.
[35] Yunpeng Pan, Ching-An Cheng, Kamil Saigol, Keuntaek Lee, Xinyan Yan, Evangelos Theodorou, and Byron Boots. Agile autonomous driving using end-to-end deep imitation learning. arXiv preprint arXiv:1709.07174, 2017.
[36] Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. Moment matching for multi-source domain adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1406-1415, 2019.
[37] Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine. Advantage-weighted regression: Simple and scalable off-policy reinforcement learning. arXiv preprint arXiv:1910.00177, 2019.
[38] Jan Peters and Stefan Schaal. Reinforcement learning by reward-weighted regression for operational space control. In Machine Learning, Proceedings of the 24th International Conference on Machine Learning, pages 745-750, 2007.
[39] Neoklis Polyzotis and Matei Zaharia. What can data-centric ai learn from data and ml engineering? arXiv preprint arXiv:2112.06439, 2021.
[40] Dean Pomerleau. Efficient training of artificial neural networks for autonomous navigation. Neural Computation, 3(1):88-97, 1991.
[41] Martin L. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, 2014.
[42] Nived Rajaraman, Lin F. Yang, Jiantao Jiao, and Kannan Ramchandran. Toward the fundamental limits of imitation learning. In Advances in Neural Information Processing Systems 33, pages 2914-2924, 2020.
[43] Nived Rajaraman, Yanjun Han, Lin Yang, Jingbo Liu, Jiantao Jiao, and Kannan Ramchandran. On the value of interaction and function approximation in imitation learning. In Advances in Neural Information Processing Systems 34, pages 1325-1336, 2021.
[44] Stéphane Ross and Drew Bagnell. Efficient reductions for imitation learning. In Proceedings of the 13rd International Conference on Artificial Intelligence and Statistics, pages 661–668, 2010.
[45] Fumihiro Sasaki and Ryota Yamashina. Behavioral cloning from noisy demonstrations. In Proceedings of the 9th International Conference on Learning Representations, 2021.
[46] Alexander Shapiro. Monte carlo sampling methods. Handbooks in operations research and management science, 10:353-425, 2003.
[47] Harshit Sikchi, Qinqing Zheng, Amy Zhang, and Scott Niekum. Dual rl: Unification and new methods for reinforcement and imitation learning. In Sixteenth European Workshop on Reinforcement Learning, 2023.
[48] Jonathan Spencer, Sanjiban Choudhury, Arun Venkatraman, Brian Ziebart, and J Andrew Bagnell. Feedback in imitation learning: The three regimes of covariate shift. arXiv preprint arXiv:2102.02872, 2021.
[49] Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Bünau, and Motoaki Kawanabe. Direct importance estimation with model selection and its application to covariate shift adaptation. In Advances in Neural Information Processing Systems 20, pages 1433-1440, 2007.
[50] Umar Syed, Michael H. Bowling, and Robert E. Schapire. Apprenticeship learning using linear programming. In William W. Cohen, Andrew McCallum, and Sam T. Roweis, editors, Proceedings of the 25th International Conference on Machine Learning, pages 1032-1039, 2008.
[51] Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, and Masashi Sugiyama. Variational imitation learning with diverse-quality demonstrations. In Proceedings of the 37th International Conference on Machine Learning, pages 9407-9417, 2020.